HTTP 200 and “Something went wrong” can appear in the same API response because HTTP status and application-level outcome are separate signals. Whether that means failure depends on the endpoint’s documented contract. If your client retries without checking that contract, those extra requests may add to rate-limit pressure—but the status and message alone do not establish how a particular service counted them.
What HTTP 200 does—and does not—tell you
HTTP 200 is a status code, but it does not universally prove that the requested operation succeeded according to the API’s application-level rules. Some APIs put an application error in the response body even when the HTTP response is 200. Others document 200 as success. The endpoint’s contract determines how to interpret the combination.
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For example, monday.com’s error-handling documentation says application-level errors can return HTTP 200, with an errors array in the JSON body; partial data may also be present. By contrast, GitLab’s REST API documentation and News API’s error documentation describe 200 as success under their respective conventions. These examples are not a universal rule: check the exact provider and endpoint you called.
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A generic message tells you little by itself. It might accompany a structured application error, a response containing partial data, or an error format specific to that API. The title alone does not identify the service or endpoint, so it cannot establish what that particular response meant.
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Inspect the complete response and the API’s documented fields. monday.com, for example, documents an errors array and a request_id that can help identify a request when investigating a failure. Check for structured error codes and details, partial data, and any relevant response headers. Redact credentials, tokens, personal information, and other secrets before retaining or sharing logs.
Can retries count against a rate limit?
A retry is another request. If your client sends it, it can add request volume and increase pressure on a rate limit. OpenAI’s error-code guidance warns about frequent or concurrent requests and advises: “Pace your requests and avoid making unnecessary or redundant calls.” That makes retries a sensible cause to investigate, not proof that a specific request counted against a particular limit. The provider, endpoint, rate-limit policy, and actual request history determine that.
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Retries can also multiply when more than one layer is responsible. An application may retry a failed call while an SDK retries underneath it. Check both configurations and count total attempts, not just the retries visible in your own code.
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How to investigate the response and retry loop
- Identify the provider and exact endpoint. Read that endpoint’s response, error, and retry documentation rather than inferring behavior from another API’s conventions.
- Capture the response safely. Log the HTTP status alongside the structured body, error code, request ID, and relevant headers. Remove secrets and personal data.
- Interpret the body under the endpoint’s contract. Check for an application-level error and for partial data; do not treat the generic message or the 200 status alone as conclusive.
- Inspect rate-limit information. If the API documents rate-limit headers or a
Retry-Afterheader, record their values for the response. - Count attempts across layers. Review application code, SDK settings, and any intermediate client layers to find nested automatic retries.
- Classify the error before retrying. Retry only when the provider’s guidance supports it; an account, validation, permission, or exhausted-quota problem generally needs correction rather than another identical request.
How to retry without adding avoidable pressure
When the response provides Retry-After
Follow the server-provided delay when the API documents Retry-After. OpenAI’s error guidance and GitLab’s rate-limit guidance instruct clients to wait at least that long when it is supplied. Do not turn a server-requested wait into an immediate retry.
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If the error is transient and the provider gives no retry delay, OpenAI recommends exponential backoff with jitter and a limited number of retries. In practical terms, increase the wait between attempts, add a random offset so clients do not all retry together, and stop after a defined attempt limit. Also reduce concurrency or request volume if traffic is contributing to throttling.
When another retry will not fix the cause
Do not keep retrying errors that require a change to the account or request. OpenAI specifically notes that billing, spend, or quota errors will not be resolved by retrying; address the underlying limit or account condition. Other APIs may classify errors differently, so use their documented codes and guidance rather than assuming the same rules apply.
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A provider-specific wrinkle: OpenAI’s slow_down response
OpenAI documents a slow_down condition associated with traffic increasing too quickly. Its guidance says this can occur even when requests-per-minute and tokens-per-minute limits have not been exceeded. For that OpenAI-specific case, it recommends honoring Retry-After, reducing request rate, and ramping traffic gradually. This behavior should not be generalized to providers that have not documented it.
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